Bibliographic record
Abstract
There is a significant body of literature that explores the theory and methodology behind the inclusion of citizen voices in decisions which impact public institutions and assets and are made in the “public interest”. In Canada, this includes the concept of meaningful engagement, often discussed in the context of environmental governance. Principles of meaningful engagement are historically underpinned by a suite of techniques across different spectrums (e.g., tools to share information such as advertisements and open houses, through opportunities for meaningful collaboration, such as shared decision-making). Broadly speaking, an effective engagement program should be transparent, accountable, open to all, ensure early and ongoing engagement, and have provisions to fund participation. Increasingly, there has been recognition that decision-makers should also provide evidence that stakeholder voices were heard during the process, such as through publicly available reasons for the decision. Decisions should demonstrate consideration of stakeholder submissions, ensure transparency and accountability, and be based on the principles of natural justice.The purpose of this chapter is to consider how different voices are recognized across a range of administrative tribunals in Canada. A legislative review identified fifty-five tribunals that provide advice and guidance to government, with legislative requirements and recent opportunities for participation. Based on this review, four categories of administrative tribunal emerge: natural resources (federal & provincial/territorial); utilities (provincial/territorial); financial & consumer services (federal & provincial); and primarily federal matters (communications, patent medicines and transportation).While the engagement techniques are broadly explored in the legislation, small case studies provide additional details, particularly related to the role citizen voices have in the decisions. What emerges is that public access to meaningful participation opportunities varies considerably depending on the jurisdiction and the type of tribunal. Although good practices were noted in each case, there is substantial room for improvement in order to fully democratize the regulatory decision-making process in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".